Technical aspects of the Parkes error grid

Accuracy and feasibility of continuous glucose monitoring system in pancreatectomy patients

PURPOSE

Pancreatectomy patients often experience challenging fluctuations in blood glucose levels; therefore, they require a reliable monitoring system. This study aimed to determine the accuracy and acceptability of a continuous glucose monitoring (CGM) system compared with the intermittent capillary glucose test in patients who have undergone pancreatectomy.

METHODS

Thirty non-diabetic pancreatectomy patients participated. We used the FreeStyle Libre Flash Glucose Monitoring System (Abbott Diabetes Care) for continuous interstitial glucose monitoring. Capillary reference glucose levels were checked four times daily. Accuracy was checked using the Clarke Error Grid.

RESULTS

The mean age of the participants was 56.8 ± 12.0 years, of whom 61.3% underwent pancreaticoduodenectomy and 38.7% underwent distal pancreatectomy. Three patients developed pancreatogenic diabetes after pancreatectomy. The clinical accuracy of continuous glucose monitoring compared with capillary glucose was 43.9% in Zone A (clinically accurate zone) and 99.8% in Zone A + B (low risk of error) of the Clarke Error Grid. No device-related adverse events were reported. Patients rated favorable user acceptability on the questionnaire.

CONCLUSION

This pilot study demonstrated that the CGM device is accurate and safe for patients who underwent pancreatectomy, with favorable user acceptability. Despite these challenges, the study proposes that the CGM device is beneficial for monitoring glucose levels after discharge in patients with impaired glucose levels following pancreatectomy.

Evaluating the precision and reliability of real-time continuous glucose monitoring systems in ambulatory settings: a systematic review

Background

Continuous glucose monitoring (CGM) with minimally invasive devices plays a key role in the assessment of daily diabetes management by detecting and alerting to potentially dangerous trends in glucose levels, improving quality of life, and treatment adherence. However, there is still uncertainty as to whether CGMs are accurate enough to replace self-monitoring of blood glucose, especially in detecting episodes of hypoglycemia.

Objectives

Evaluate clinical, numerical accuracy, sensitivity, and specificity of the CGM devices commercially available when compared to the reference standard of arterial or venous blood glucose.

Data sources and methods

We searched the Cochrane Library, PubMed, EMBASE, and LILACS databases. The quality was assessed with the Quality Assessment Diagnostic Accuracy Studies (QUADAS-2) tool. Clinical and numerical accuracy data were extracted. Sensitivity and specificity were calculated using Review Manager software. Heterogeneity was assessed by visual examination of forest plot and summary receiver operating characteristic curves.

Results

Twenty-two studies with a total of 2294 patients were included. The average mean absolute relative difference for overall diagnostic accuracy was 9.4%. None of the devices evaluated with ISO 15197:2013 criteria achieved values ⩾95% of measurements in the stipulated ranges in hypoglycemia (±15 mg/dL), but two devices did achieve it in hyperglycemia (±15%; Dexcom G6 and G7). Most of the devices evaluated with consensus error grids reached values above 99% in zones A and B only in overall accuracy and hyperglycemia. For hypoglycemia, the average sensitivity was 85.7% and specificity 95.33%, and for hyperglycemia was 97.45% and 96% respectively.

Conclusion

Currently available CGM devices have adequate accuracy for euglycemia and hyperglycemia; however, it is still inadequate for hypoglycemia, although it has improved over time.

Trial registration

Prospero registration ID CRD42023399767.

Evaluation of point-of-care capillary and venous blood glucose concentrations in hospitalized neonatal foals

OBJECTIVE

To compare glucose measurements from capillary and venous blood samples using a point-of-care (POC) glucometer with a standard laboratory (colorimetric, glucose oxidase) assay (LABGLU) in a population of hospitalized, neonatal foals.

DESIGN

Multicenter, prospective, experimental study, conducted between March 2019 and June 2020.

SETTING

Four university teaching hospitals and 1 private referral hospital.

ANIMALS

Fifty-four hospitalized neonatal (≤30 days of age) foals.

INTERVENTIONS

Simultaneous capillary (muzzle, POCMUZ) and venous (jugular, POCJUG) blood samples were obtained to determine POC glucose concentrations. Venous samples were also analyzed by LABGLU. Each foal was sampled at the time of enrollment or admission to the hospital and at 1 subsequent point during hospitalization. Indirect mean arterial pressure and hematocrit were concurrently recorded.

MEASUREMENTS AND MAIN RESULTS

Bland-Altman analysis showed a mean bias (95% limits of agreement) of -28.0 (-88.6 to 32.6) mg/dL for comparison of POCJUG with LABGLU, -8.2 (-94.3 to 78.0) mg/dL for POCMUZ and LABGLU, and 18.8 (-44.4 to 82.0) mg/dL for POCMUZ and POCJUG. A total of 63.5% of the POCJUG and 45.2% of the POCMUZ samples exceeded the reference value by ±15 mg/dL (for LABGLU samples <75 mg/dL) or ±15% (for LABGLU samples ≥75mg/dL). Concordance correlation coefficient (95% confidence interval [CI]) indicated a fair agreement between POCJUG and LABGLU (0.75, 95% CI: 0.66-0.82) and between POCMUZ and LABGLU (0.71, 95% CI: 0.58-0.80). Fifty percent (14/28) of hypoglycemic foals on the reference method were incorrectly classified as euglycemic by POCJUG, and 5 of 28 were incorrectly classified by POCMUZ.

CONCLUSIONS

In the sampled population, the chosen POC glucometer lacked agreement with the standard laboratory measurement. Limits of agreement were wide for both POCJUG and POCMUZ. Inaccuracies in POC results could impact decision-making in the clinical management of glycemic control in hospitalized neonatal foals and, importantly, increase the risk of hypoglycemic events being underdiagnosed in critical patients.

Performance of Continuous Glucose Monitoring System Among Patients With Acute Ischaemic Stroke Treated With Mechanical Thrombectomy

AIMS

Glucose metabolism abnormalities are prevalent in acute ischaemic stroke (AIS) patients and are associated with poor prognosis. The continuous glucose monitoring (CGM) system can provide detailed information on glucose levels and glycaemic excursions. This study aimed to evaluate the feasibility and accuracy of CGM application in the acute phase of AIS patients.

METHODS

This single-centre, prospective, and observational study consecutively enrolled patients with AIS with anterior circulation large vessel occlusion (AC-LVO) and received mechanical thrombectomy (MT) within 24 h of symptom onset. A user-retrospectively calibrated iPro2 CGM system was implanted right before the MT procedure started and removed on the fifth day after MT or at discharge. Fingertip glucose was measured as a reference. Accuracy evaluation included the Bland-Altman plot (with a proportion of CGM values within 15/15, 20/20 and 30/30), the absolute relative difference (ARD) and error grid analysis (EGA). The safety and glucose profiles were also evaluated.

RESULTS

Of the 183 patients screened, 141 were included, with a median monitoring duration of 4.49 days. Compared to reference measurements, 3097 CGM readings were matched with a mean bias of -4.16 mg/dL. The proportions of sensor readings meeting the 15/15, 20/20 and 30/30 criteria were 64.55%, 76.07% and 87.21%, respectively. The overall mean and median ARD were 14.60% ± 14.62% and 9.77% (4.15, 20.00). EGA showed that 98.97%, 99.42% and 99.06% values fall within clinically accurate zones in Clarke, Parkes and continuous glucose EGA, respectively.

CONCLUSION

The CGM system was feasible, safe and accurate for in-hospital use among AIS patients who received MT.

Combining an Electrochemical Continuous Glucose Sensor With an Insulin Delivery Cannula: A Feasibility Study

BACKGROUND

Combining a continuous glucose monitor with an insulin delivery cannula (CGM-IS) could benefit clinical outcomes. We evaluated the feasibility of a single-needle insertion electrochemical investigational CGM-IS (Pacific Diabetes Technologies, Portland, Oregon) in type 1 diabetes adults.

METHODS

Following 48 hours run-in using a Medtronic 780G in manual mode with a commercial insulin set, 12 participants commenced insulin delivery using the CGM-IS. A standardized test meal was eaten on the mornings of days 1 and 4. Venous samples were collected every 10 minutes one hour prior to and 15 minutes post-meal for four hours. CGM-IS glucose measurements were post-processed with a single capillary blood calibration during warm-up and benchmarked against YSI. A Dexcom G6 sensor was worn post-consent to study end.

RESULTS

Mean absolute relative difference (MARD) for the CGM-IS glucose measurements was 9.2% (484 paired data points). Consensus error grid revealed 88.6% within zone A and 100% in A + B. Mean (SD) % bias was -3.5 (11.7) %. There were 35 paired YSI readings <100 mg/dL cutoff and 449 ≥100 mg/dL with 81.4% within ±15 mg/dL or ±15%, and 89.9% within ±20 mg/dL or ±20%. Two cannula occlusions required discontinuation of insulin delivery: one at 70 hours post insertion and another during the day 4 meal test. Mean (SD) Dexcom glucose measurements during run-in and between meal tests was respectively 161.3 ± 27.3 mg/dL versus 158.0 ± 25.6 mg/dL; P = .39 and corresponding mean total daily insulin delivered by the pump was 58.0 ± 25.4 Units versus 57.1 ± 28.8 Units; P = .47.

CONCLUSIONS

Insulin delivery and glucose sensing with the investigational CGM-IS was feasible. Longer duration studies are needed.

Accuracy and validation of a point-of-care blood glucose monitoring system for use in horses

Abnormal blood glucose (BG) levels often seen in critically ill horses are significantly associated with adverse patient outcomes and increased mortality. Rapid and accurate BG monitoring is now considered an essential component of evidence-based equine practice and can provide critical information quickly for treatment. Although several point-of-care (POC) BG monitoring hand-held devices are commercially available for veterinary use, none contains a unique algorithm validated for use in horses. The AlphaTrak 3 (AT3) BG monitoring system is a first-of-its-kind device with an equine-specific algorithm that allows stall-side clinical decision making, and frequent monitoring at minimal cost. As such, AT3 is potentially a preferred alternative to more costly and time-consuming standard diagnostic reference laboratory methods. The objective of this study was to determine the accuracy of the AT3 device in measuring BG levels in equine whole blood samples in comparison to results obtained by the Beckman Coulter AU480 reference analyzer per ISO15197:2013 specifications. Accuracy of the AT3 equine algorithm were initially verified by testing equine blood samples with artificially adjusted blood glucose levels followed by its validation in a field study. Testing with artificially adjusted equine samples (n = 129) showed that 98.9% of glucose measurements ranging from 29 to 479 mg/dL fell within ISO accuracy threshold of ±15 mg/dL or ±15% of the average reference value. In addition, 100% of the AT3 measurements fell in consensus error grid (CEG) zone A, which indicates that test outcomes have a minimal likelihood of adverse clinical impact. In a follow-up field study involving 96 horses, 98.4% of AT3 measurements met the ISO accuracy threshold and 99.2% of AT3 measurements fell in CEG zone A. These results demonstrate that the AT3 glucometer has a high degree of accuracy in horses and is a dependable, convenient, and cost-effective device for accurately monitoring equine BG levels in farm or clinical settings.

A wearable, rapidly manufacturable, stability-enhancing microneedle patch for closed-loop diabetes management

The development of a wearable, easy-to-fabricate, and stable intelligent minisystem is highly desired for the closed-loop management of diabetes. Conventional systems always suffer from large size, high cost, low stability, or complex fabrication. Here, we show for the first time a wearable, rapidly manufacturable, stability-enhancing microneedle patch for diabetes management. The patch consists of a graphene composite ink-printed sensor on hollow microneedles, a polyethylene glycol (PEG)-functionalized electroosmotic micropump integrated with the microneedles, and a printed circuit board for precise and intelligent control of the sensor and pump to detect interstitial glucose and deliver insulin through the hollow channels. Via synthesizing and printing the graphene composite ink, the sensor fabrication process is fast and the sensing electrodes are stable. The PEG functionalization enables the micropump a significantly higher stability in delivering insulin, extending its lifetime from days to weeks. The patch successfully demonstrated excellent blood glucose control in diabetic rats. This work may introduce a new paradigm for building new closed-loop systems and shows great promise for widespread use in patients with diabetes.

A self-powered and supercapacitive microneedle continuous glucose monitoring system with a wide range of glucose detection capabilities

Continuous detection of sudden changes in blood glucose is essential for individuals with diabetes who have difficulty in maintaining optimal control of their blood glucose levels. Hypoglycemic shock or a hyperglycemic crisis are likely to occurs in patients with diabetes and poses a significant threat to their lives. Currently, commercial continuous glucose monitoring (CGM) has limits in the glucose concentration detection range, which is 40-500 mg/dL, making it difficult to prevent the risk of hyperglycemic shock. In addition, current CGMs are invasive, cause pain and irritation during usage, and expensive. In this research, we overcome these limitations by introducing a novel mechanism to detect glucose concentration using supercapacitors. The developed CGM, which is self-powered and minimally invasive due to the use of microneedles, can detect a wider range of glucose concentrations than commercial sensors. In addition, efficacy and stability were proven through in vitro and in vivo experiments. Thus, this self-powered, microneedle and supercapacitive-type CGM can potentially prevent both hypoglycemic and complications of hyperglycemia without pain and with less power consumption than current commercial sensors.

Accuracy of an Off-Label Transmitter and Data Manager Paired With an Intermittent Scanned Continuous Glucose Monitor in Adults With Type 1 Diabetes

BACKGROUND

This work evaluates the accuracy and agreement between the FreeStyle Libre sensor (FSL) and an off-label converted real-time continuous glucose monitor (c-rtCGM) device consisting of the MiaoMiao transmitter and the xDrip+ application which can be coupled to the FSL.

METHODS

Four weeks of glucose data were collected from 21 participants with type 1 diabetes using the c-rtCGM and FSL: two weeks with a single initial calibration (uncalibrated) and two weeks with a daily calibration (calibrated). Accuracy and agreement evaluation included mean absolute relative difference (MARD), the %20/20 rule, Bland-Altman plots, and the Consensus Error Grid analysis.

RESULTS

Values reported by the c-rtCGM system compared with the FSL resulted in an overall MARD of 12.06% and 84.71% of the results falling within Consensus Error Grid Zone A when the device is calibrated. For uncalibrated devices, an overall MARD of 17.49% was obtained. Decreased accuracy was shown in the hypoglycemic range and for rates of change greater than 2 mg/dL/min. The between-device bias also incremented with increasing glucose values.

CONCLUSION

Measurements recorded by the c-rtCGM were found to be accurate when compared with FSL data only when performing daily c-rtCGM device calibrations. High drops in accuracy and agreement between devices occurred when the c-rtCGM was not calibrated.

Glu-Ensemble: An ensemble deep learning framework for blood glucose forecasting in type 2 diabetes patients

Diabetes is a chronic metabolic disorder characterized by elevated blood glucose levels, posing significant health risks such as cardiovascular disease, and nerve, kidney, and eye damage. Effective management of blood glucose is essential for individuals with diabetes to mitigate these risks. This study introduces the Glu-Ensemble, a deep learning framework designed for precise blood glucose forecasting in patients with type 2 diabetes. Unlike other predictive models, Glu-Ensemble addresses challenges related to small sample sizes, data quality issues, reliance on strict statistical assumptions, and the complexity of models. It enhances prediction accuracy and model generalizability by utilizing larger datasets and reduces bias inherent in many predictive models. The framework's unified approach, as opposed to patient-specific models, eliminates the need for initial calibration time, facilitating immediate blood glucose predictions for new patients. The obtained results indicate that Glu-Ensemble surpasses traditional methods in accuracy, as measured by root mean square error, mean absolute error, and error grid analysis. The Glu-Ensemble framework emerges as a promising tool for blood glucose level prediction in type 2 diabetes patients, warranting further investigation in clinical settings for its practical application.

Difference in the accuracy of the third-generation algorithm and the first-generation algorithm of FreeStyle Libre continuous glucose monitoring device

BACKGROUND

FreeStyle Libre uses the algorithm to calculate the sensor glucose (SG) levels. The manufacturer announced that they had changed the algorithm from the first generation (Gen. 1) to the third generation (Gen. 3). To assess the difference, we conducted an observational study to analyze the characteristics of the measurements by these two algorithms compared to the capillary blood glucose (BG) levels.

METHODS

Participants with type 1 diabetes wore two FreeStyle Libre sensors, one on the left arm used with Gen. 3 algorithm, and another on the right arm used in combination with the FreeStyle Libre Reader with Gen. 1 algorithm.

RESULTS

Data were collected from 11 participants. The Bland-Altman analysis of the measurements by Gen. 3 algorithm showed bias of 7.4 mg/dl and no proportional bias was observed (r=0.130). In contrast, the Bland-Altman analysis of the measurements by Gen. 1 algorithm showed bias of 4.4 mg/dl and proportional bias was observed (r=0.424). The MARD of Gen. 3 algorithm and Gen. 1 algorithm was 11.9±9.0% and 9.7±8.3%, respectively (P=0.053).

CONCLUSION

No proportional bias in the measurements by Gen. 3 algorithm was observed, but in those by Gen. 1 algorithm. J. Med. Invest. 71 : 225-231, August, 2024.

System Accuracy and Interference Evaluation of a New Glucose Dehydrogenase-Based Blood Glucose Meter for Patient Self-Testing

New European medical device regulations require the performance of postmarketing surveillance evaluations for blood glucose meters (BGMs). We conducted an ISO15197:2015-conform system performance evaluation with the approved glucose dehydrogenase (GDH)-based Wellion NEWTON BGM. One hundred subjects were enrolled into the study (44 female, 56 male, 43 healthy subjects, 23 type 1 diabetes, 34 type 2 diabetes, age: 53.7 ± 15.8 years). In addition, manipulated heparinized whole blood was used for a laboratory interference test with ten selected substances (interference definition: substance-induced bias > 10%). The mean absolute relative difference (MARD) was 4.7%, and 100% of the values were in zones A (99.7%) and B (0.3%), respectively, of the consensus error grid. Interference was observed with xylose only, which is a known interfering substance for GDH-based BGMs.

Accuracy of Point-of-Care Blood Glucometers in Neonates and Critically Ill Adults

PURPOSE

Inpatient glycemic management has become a common issue because of the increasing number of hospitalized patients with hyperglycemia. Point-of-care devices can enable timely inpatient glucose monitoring, which may lead to better outcomes. The accuracy of point-of-care testing in various clinical scenarios has been questioned, particularly in neonates and critically ill patients. This study aimed to evaluate the accuracy of the CONTOUR PLUS and CONTOUR PLUS ONE glucometers (new wireless systems that link to a smart mobile device) when used as point-of-care devices for blood glucose monitoring in neonates and critically ill adults in inpatient settings.

METHODS

This cross-sectional study was conducted at a medical center in central Taiwan and enrolled patients admitted to the neonatal intensive care unit, sick child room, or respiratory intensive care unit between November 2020 and April 2021. Neonates with suspected infection or abnormal blood coagulation and adults who had abnormal blood coagulation, were pregnant, had received organ transplants, or had undergone massive blood transfusions were excluded. The accuracy of the glucometers was determined based on the following criteria of the International Organization for Standardization (ISO) standard: 15197:2013.

FINDINGS

Overall, 114 neonates (mean age, 4.2 days [range, 0-28 days]; 65 boys [57.0%]) and 106 hospitalized critically ill adults (mean age, 68.2 years [range, 27-94 years]; 72 men [67.9%]) were enrolled in this study. The glucose values obtained with each glucometer had good precision, and all findings met the reference criteria of the within-lot results. All measurements of the neonates' venous blood by each glucometer met the accuracy criteria specified by ISO standard 15197:2013. Furthermore, 98.1% and 97.2% of the arterial blood glucose measurements for critically ill adults obtained with CONTOUR PLUS and CONTOUR PLUS ONE met the accuracy criteria, respectively.

IMPLICATIONS

Both glucose management systems met the accuracy criteria for venous blood from neonates and arterial blood from critically ill adults. Thus, the use of these 2 point-of-care devices in inpatient settings, including for neonates and critically ill adults, can be recommended to minimize limitations associated with the clinical application of point-of-care testing in glucose management. The wireless connection may play a role in the subsequent development of institution-wide virtual glycemic management under the supervision of a team of endocrinologists.

Miniaturization of an Osmotic Pressure-Based Glucose Sensor for Continuous Intraperitoneal and Subcutaneous Glucose Monitoring by Means of Nanotechnology

The Sencell sensor uses glucose-induced changes in an osmotic pressure chamber for continuous glucose measurement. A final device shall have the size of a grain of rice. The size limiting factor is the piezo-resistive pressure transducers inside the core sensor technology (resulting chamber volume: 70 µL. To achieve the necessary miniaturization, these pressure transducers were replaced by small (4000 × 400 × 150 nm³) nano-granular tunneling resistive (NTR) pressure sensors (chamber volume: 750 nL). For benchmark testing, we filled the miniaturized chamber with bovine serum albumin (BSA, 1 mM) and exposed it repeatedly to distilled water followed by 1 mM BSA solution. Thereafter, we manufactured sensors with glucose testing chemistry (ConcanavalinA/dextran) and investigated sensor performance with dynamic glucose changes between 0 and 300 mg/dL. Evaluation of the miniaturized sensors resulted in reliable pressure changes, both in the BSA benchmark experiment (30-35 mBar) and in the dynamic in vitro continuous glucose test (40-50 mBar). These pressure results were comparable to similar experiments with the previous larger in vitro sensors (30-50 mBar). In conclusion, the NTR pressure sensor technology was successfully employed to reduce the size of the core osmotic pressure chamber by more than 95% without loss in the osmotic pressure signal.

Constrained IoT-Based Machine Learning for Accurate Glycemia Forecasting in Type 1 Diabetes Patients

Individuals with diabetes mellitus type 1 (DM1) tend to check their blood sugar levels multiple times daily and utilize this information to predict their future glycemic levels. Based on these predictions, patients decide on the best approach to regulate their glucose levels with considerations such as insulin dosage and other related factors. Nevertheless, modern developments in Internet of Things (IoT) technology and innovative biomedical sensors have enabled the constant gathering of glucose level data using continuous glucose monitoring (CGM) in addition to other biomedical signals. With the use of machine learning (ML) algorithms, glycemic level patterns can be modeled, enabling accurate forecasting of this variable. Constrained devices have limited computational power, making it challenging to run complex machine learning algorithms directly on these devices. However, by leveraging edge computing, using lightweight machine learning algorithms, and performing preprocessing and feature extraction, it is possible to run machine learning algorithms on constrained devices despite these limitations. In this paper we test the burdens of some constrained IoT devices, probing that it is feasible to locally predict glycemia using a smartphone, up to 45 min in advance and with acceptable accuracy using random forest.

Numerical and clinical precision in hypoglycemia of the intermittent FreeStyle Libre glucose monitoring through an NFC-Bluetooth transmitter associated with the xDrip+ algorithm in diabetic patients under insulin therapy

INTRODUCTION

There are data capture devices that attach to the FreeStyle Libre sensor and convert its communication from NFC (Near-field communication) to Bluetooth technology, generating real-time continuous glucose monitoring. The accuracy of hypoglycemia measurements displayed by smartphone apps using this device has not been established.

METHODS

Study of diagnostic tests. Numerical accuracy was evaluated, utilizing the absolute difference with respect to capillary glucometry (ISO 15197:2015 standard) and clinical accuracy, using the Clarke and Parkes (Consensus) error grids, for glucose measurements less than 70mg/dL performed with the FreeStyle Libre system and with the digital estimation xDrip+ app, in diabetic patients managed with insulin therapy.

RESULTS

Twenty-seven patients were included (TIR 73.4%, TBR70 5.6%), who contributed 83 hypoglycemic events. Numerical accuracy was adequate in similar proportions with the FreeStyle Libre system compared to the xDrip+ app (81.92% vs. 68.67%, p=0.0630). The clinical accuracy evaluation showed that 92.8% of the measurements for xDrip+ and 98.8% for FreeStyle libre met the criteria according to the Parkes (Consensus) grid (p=0.0535); and 79.5% and 91.6% of the measurements met the criteria according to the Clarke grid (p=0.0273), being higher with FreeStyle libre.

CONCLUSIONS

The use of the NFC-Bluetooth transmitter (Miao-Miao) associated with the xDrip+ app does not improve numerical or clinical accuracy for detecting hypoglycemic events in diabetic patients managed with insulin therapy, compared to the FreeStyle Libre device.

A wearable, minimally-invasive, fully electrochemically-controlled feedback minisystem for diabetes management

Diabetes is a chronic disease affecting 10% of the population globally, and can lead to serious damage in the heart, kidneys, eyes, blood vessels or nerves. Commercial artificial closed-loop feedback systems can significantly improve diabetes management and save lives. However, they are large and expensive for users. Here, we demonstrate for the first time a wearable, minimally-invasive, fully electrochemically-controlled feedback minisystem for diabetes management. Both the working principles of the sensor and pump in the feedback system are based on electrochemical reactions. The smart minisystem was constructed based on integrating the thermoplastic polyurethane hollow microneedles with an electrochemical biosensing device on its outer layer and an electrochemical micropump facing the inner layer of the microneedles. The sensing device was constructed based on sputtering thin metal films through a shadow mask and electroplating Prussian blue on the surface of the microneedles, followed by the immobilization of glucose oxidase on the working electrode. The electrochemical micropump was constructed by sputtering the interdigital electrodes, followed by sealing with a thin elastic film, which was further integrated with the inner channels of the microneedles. Both the sensor and the pump were electrically powered. Via being controlled by a printed circuit board, the biosensing device monitored the levels of interstitial glucose continuously to drive the electrochemical pump to deliver insulin intelligently, in order to control blood glucose within the normal range. The closed-loop feedback system was studied for its capability in maintaining the blood glucose levels of diabetic rats under various physiological conditions. The utility of the intelligent feedback system was successfully demonstrated on diabetic rats for controlling the blood glucose levels within the normal range. The minisystem is wearable, small, cost-effective, precise, stable and painless. It is anticipated that this approach opens a new paradigm for the development of closed-loop diabetes minisystems and may lead to a compelling future for diabetes management.

The hypoglycaemia error grid: A UK-wide consensus on CGM accuracy assessment in hyperinsulinism

Objective

Continuous Glucose Monitoring (CGM) is gaining in popularity for patients with paediatric hypoglycaemia disorders such as Congenital Hyperinsulinism (CHI), but no standard measures of accuracy or associated clinical risk are available. The small number of prior assessments of CGM accuracy in CHI have thus been incomplete. We aimed to develop a novel Hypoglycaemia Error Grid (HEG) for CGM assessment for those with CHI based on expert consensus opinion applied to a large paired (CGM/blood glucose) dataset.

Design and methods

Paediatric endocrinology consultants regularly managing CHI in the two UK centres of excellence were asked to complete a questionnaire regarding glucose cutoffs and associated anticipated risks of CGM errors in a hypothetical model. Collated information was utilised to mathematically generate the HEG which was then approved by expert, consensus opinion. Ten patients with CHI underwent 12 weeks of monitoring with a Dexcom G6 CGM and self-monitored blood glucose (SMBG) with a Contour Next One glucometer to test application of the HEG and provide an assessment of accuracy for those with CHI.

Results

CGM performance was suboptimal, based on 1441 paired values of CGM and SMBG showing Mean Absolute Relative Difference (MARD) of 19.3% and hypoglycaemia (glucose <3.5mmol/L (63mg/dL)) sensitivity of only 45%. The HEG provided clinical context to CGM errors with 15% classified as moderate risk by expert consensus when data was restricted to that of practical use. This provides a contrasting risk profile from existing diabetes error grids, reinforcing its utility in the clinical assessment of CGM accuracy in hypoglycaemia.

Conclusions

The Hypoglycaemia Error Grid, based on UK expert consensus opinion has demonstrated inadequate accuracy of CGM to recommend as a standalone tool for routine clinical use. However, suboptimal accuracy of CGM relative to SMBG does not detract from alternative uses of CGM in this patient group, such as use as a digital phenotyping tool. The HEG is freely available on GitHub for use by other researchers to assess accuracy in their patient populations and validate these findings.

A Microtube-Based Wearable Closed-Loop Minisystem for Diabetes Management

Diabetes is a chronic metabolic disease with a high blood glucose level, leading to both seriously acute and chronic complications. The closed-loop system is an ideal system for diabetes management. However, the large size and high cost of the commercial systems restrict their widespread uses. Here, we present for the first time a microtube-based wearable closed-loop minisystem for diabetes management. The closed-loop minisystem includes a biosensing device, an electroosmotic micropump, and a printed circuit board (PCB) with an algorithm. The microtube-based sensing device coated on the outer surface of the microtube is inserted into subcutaneous tissue for detecting interstitial glucose; the current signal for sensing glucose is processed by the PCB to power the electroosmotic micropump intelligently for the delivery of insulin into the subcutaneous tissue via the microtube channel. The closed-loop minisystem worn on a diabetic SD rat can successfully maintain its blood glucose level within a safe level. It is expected that this new closed-loop paradigm could open up new prospects for clinical diabetes management.

Accuracy of a Factory-Calibrated Continuous Glucose Monitor in Individuals With Diabetes on Hemodialysis

OBJECTIVE

Continuous glucose monitoring (CGM) improves diabetes management, but its reliability in individuals on hemodialysis is poorly understood and potentially affected by interstitial and intravascular volume variations.

RESEARCH DESIGN AND METHODS

We assessed the accuracy of a factory-calibrated CGM by using venous blood glucose measurements (vBGM) during hemodialysis sessions and self-monitoring blood glucose (SMBG) at home.

RESULTS

Twenty participants completed the protocol. The mean absolute relative difference of the CGM was 13.8% and 14.4%, when calculated on SMBG (n = 684) and on vBGM (n = 624), and 98.7% and 100% of values in the Parkes error grid A/B zones, respectively. Throughout 181 days of CGM monitoring, the median time in range (70-180 mg/dL) was 38.5% (interquartile range 29.3-57.9), with 28.7% (7.8-40.6) of the time >250 mg/dL.

CONCLUSIONS

The overall performance of a factory-calibrated CGM appears reasonably accurate and clinically relevant for use in practice by individuals on hemodialysis and health professionals to improve diabetes management.

Discrepancies in glycemic metrics derived from different continuous glucose monitoring systems in adult patients with type 1 diabetes mellitus

BACKGROUND

Continuous glucose monitoring systems have been widely used but discrepancies among various brands of devices are rarely discussed. This study aimed to explore differences in glycemic metrics between FreeStyle Libre (FSL) and iPro2 among adults with type 1 diabetes mellitus (T1DM).

METHODS

Participants with T1DM and glycosylated hemoglobin of 7%-10% were included and wore FSL and iPro2 for 2 weeks simultaneously. Datasets collected on the insertion and detachment day, and those with insufficient quantity (<90%) were excluded. Agreements of measurement accuracy and glycemic metrics were evaluated.

RESULTS

A total of 40 498 paired data were included. Compared with the values from FSL, significantly higher median value was observed in iPro2 (147.6 [106.2, 192.6] vs. 144.0 [100.8, 192.6] mg/dl, p < 0.001) and the largest discordance was observed in hypoglycemic range (median absolute relative difference with iPro2 as reference value: 25.8% [10.8%, 42.1%]). Furthermore, significant differences in glycemic metrics between iPro2 and FSL were also observed in time in range (TIR) 70-180 mg/dl (TIR, 62.8 ± 12.4% vs. 58.8 ± 12.3%, p = 0.004), time spent below 70 mg/dl (4.4 [1.8, 10.9]% vs. 7.2 [5.4, 13.3]%, p < 0.001), time spent below 54 mg/dl (0.9 [0.3, 4.0]% vs. 2.6 [1.3, 5.6]%, p = 0.011), and coefficient of variation (CV, 38.7 ± 8.5% vs. 40.9 ± 9.3%, p = 0.017).

CONCLUSIONS

During 14 days of use, FSL and iPro2 provided different estimations on TIR, CV, and hypoglycemia-related parameters, which needs to be considered when making clinical decisions and clinical trial designs.

Evaluating Prediction of Continuous Clinical Values: A Glucose Case Study

BACKGROUND

It would be useful to be able to assess the utility of predictive models of continuous values before clinical trials are performed.

OBJECTIVE

The aim of the study is to compare metrics to assess the potential clinical utility of models that produce continuous value forecasts.

METHODS

We ran a set of data assimilation forecast algorithms on time series of glucose measurements from neurological intensive care unit patients. We evaluated the forecasts using four sets of metrics: glucose root mean square (RMS) error, a set of metrics on a transformed glucose value, the estimated effect on clinical care based on an insulin guideline, and a glucose measurement error grid (Parkes grid). We assessed correlation among the metrics and created a set of factor models.

RESULTS

The metrics generally correlated with each other, but those that estimated the effect on clinical care correlated with others the least and were generally associated with their own independent factors. The other metrics appeared to separate into those that emphasized errors in low glucose versus errors in high glucose. The Parkes grid was well correlated with the transformed glucose but not the estimation of clinical care.

DISCUSSION

Our results indicate that we need to be careful before we assume that commonly used metrics like RMS error in raw glucose or even metrics like the Parkes grid that are designed to measure importance of differences will correlate well with actual effect on clinical care processes. A combination of metrics appeared to explain the most variance between cases. As prediction algorithms move into practice, it will be important to measure actual effects.

Physiological reconstruction of blood glucose level using CGMS-signals only

Patient with diabetes must regularly monitor blood glucose level. Drawing a blood sample is a painful and discomfort experience. Alternatively, the patient measures interstitial fluid glucose level with a sensor installed in subcutaneous tissue. Then, a model of glucose dynamics calculates blood glucose level from the sensor-measured, i.e., interstitial fluid glucose level of subcutaneous tissue. Interstitial fluid glucose level can significantly differ from blood glucose level. The sensor is either factory-calibrated, or the patient calibrates the sensor periodically by drawing blood samples, when glucose levels of both compartments are steady. In both cases, the sensor lifetime is limited up to 14 days. This is the present state of the art. With a physiological model, we would like to prolong the sensor lifetime with an adaptive approach, while requiring no additional blood sample. Prolonging sensor’s lifetime, while reducing the associated discomfort, would considerably improve patient’s quality of life. We demonstrate that it is possible to determine personalized model parameters from multiple CGMS-signals only, using an animal experiment with a hyperglycemic clamp. The experimenter injected separate glucose and insulin boluses to trigger rapid changes, on which we evaluated the ability to react to non-steady glucose levels in different compartments. With the proposed model, 70%, 80% and 95% of the calculated blood glucose levels had relative error less than or equal to 21.9%, 32.5% and 43.6% respectively. Without the model, accuracy of the sensor-estimated blood glucose level decreased to 39.4%, 49.9% and 99.0% relative errors. This confirms feasibility of the proposed method.

Performance of intermittently scanned continuous glucose monitoring systems in people with type 1 diabetes: A pooled analysis

AIMS

To conduct a pooled analysis to assess the performance of intermittently scanned continuous glucose monitoring (isCGM) in association with the rate of change in sensor glucose in a cohort of children, adolescents, and adults with type 1 diabetes.

MATERIAL AND METHODS

In this pooled analysis, isCGM system accuracy was assessed depending on the rate of change in sensor glucose. Clinical studies that have been investigating isCGM accuracy against blood glucose, accompanied with collection time points were included in this analysis. isCGM performance was assessed by means of median absolute relative difference (MedARD), Parkes error grid (PEG) and Bland-Altman plot analyses.

RESULTS

Twelve studies comprising 311 participants were included, with a total of 15 837 paired measurements. The overall MedARD (interquartile range) was 12.7% (5.9-23.5) and MedARD differed significantly based on the rate of change in glucose (P < 0.001). An absolute difference of -22 mg/dL (-1.2 mmol/L) (95% limits of agreement [LoA] 60 mg/dL (3.3 mmol/L), -103 mg/dL (-5.7 mmol/L)) was found when glucose was rapidly increasing (isCGM glucose minus reference blood glucose), while a -32 mg/dL (1.8 mmol/L) (95% LoA 116 mg/dL (6.4 mmol/L), -51 mg/dL (-2.8 mmol/L)) absolute difference was observed in periods of rapidly decreasing glucose.

CONCLUSIONS

The performance of isCGM was good when compared to reference blood glucose measurements. The rate of change in glucose for both increasing and decreasing glucose levels diminished isCGM performance, showing lower accuracy during high rates of glucose change.

Diabetes Complications and Related Comorbidities Impair the Accuracy of FreeStyle Libre, a Flash Continuous Glucose Monitoring System, in Patients with Type 2 Diabetes

BACKGROUND

Although flash continuous glucose monitoring systems (FCGM) accuracy has been extensively studied in diabetes, its accuracy is still not fully evaluated in type 2 diabetes (T2D) patients in real-world settings. In the present study, we aim to assess the effects of diabetes complications and related comorbidities on FCGM accuracy in T2D patients with diabetes complications and related comorbidities in the real world.

METHODS

FCGM data were collected at eight-time points daily (3 AM, 7 AM, 9 AM, 11 AM, 1 PM, 5 PM, 7 PM, and 9 PM) from 742 patients with T2D and compared with simultaneous fingertip capillary blood glucose (reference blood glucose, REF), and the difference was evaluated using Parkes error grid (PEG), surveillance error grid (SEG), and logistic regression analysis.

RESULTS

In total, 25,579 FCGM/REF data pairs were included in the study. The FCGM values were lower than the paired REF values in 75% of the pairs. The maximum bias (-23.0%) and maximum mean absolute relative difference (24.5%) were observed at 3 AM among eight-time points. SEG analysis also demonstrated the highest percentage of paired readings in moderate and great risk zone (C and D) at 3 AM than PEG analysis (7.33% vs 0.43%, P<0.001). According to the SEG classification, hypoglycemia, infection, diabetic foot, diabetic ketoacidosis, and hypertension were independent risk factors that impaired FCGM accuracy in patients.

CONCLUSION

FCGM commonly underestimates blood glucose levels. Compared with PEG, SEG analysis seems more conducive to the analysis of FCGM performance. The present data highlights the impairment of diabetes complications and related comorbidities on the FCGM accuracy in T2D patients.

Glucose biosensors in clinical practice: principles, limits and perspectives of currently used devices

The demand of glucose monitoring devices and even of updated guidelines for the management of diabetic patients is dramatically increasing due to the progressive rise in the prevalence of diabetes mellitus and the need to prevent its complications. Even though the introduction of the first glucose sensor occurred decades ago, important advances both from the technological and clinical point of view have contributed to a substantial improvement in quality healthcare. This review aims to bring together purely technological and clinical aspects of interest in the field of glucose devices by proposing a roadmap in glucose monitoring and management of patients with diabetes. Also, it prospects other biological fluids to be examined as further options in diabetes care, and suggests, throughout the technology innovation process, future directions to improve the follow-up, treatment, and clinical outcomes of patients.

Products for Monitoring Glucose Levels in the Human Body With Noninvasive Optical, Noninvasive Fluid Sampling, or Minimally Invasive Technologies

BACKGROUND

Conventional home blood glucose measurements require a sample of blood that is obtained by puncturing the skin at the fingertip. To avoid the pain associated with this procedure, there is high demand for medical products that allow glucose monitoring without blood sampling. In this review article, all such products are presented.

METHODS

In order to identify such products, four different sources were used: (1) PubMed, (2) Google Patents, (3) Diabetes Technology Meeting Startup Showcase participants, and (4) experts in the field of glucose monitoring. The information obtained were filtered by using two inclusion criteria: (1) regulatory clearance, and/or (2) significant coverage in Google News starting in the year 2016, unless the article indicated that the product had been discontinued. The identified bloodless monitoring products were classified into three categories: (1) noninvasive optical, (2) noninvasive fluid sampling, and (3) minimally invasive devices.

RESULTS

In total, 28 noninvasive optical, 6 noninvasive fluid sampling, and 31 minimally invasive glucose monitoring products were identified. Subsequently, these products were characterized according to their regulatory, technological, and consumer features. Products with regulatory clearance are described in greater detail according to their advantages and disadvantages, and with design images.

CONCLUSIONS

Based on favorable technological features, consumer features, and other advantages, several bloodless products are commercially available and promise to enhance diabetes management. Paths for future products are discussed with an emphasis on understanding existing barriers related to both technical and non-technical issues.

Glucose Concentrations from Continuous Glucose Monitoring Devices Compared to Those from Blood Plasma during an Oral Glucose Tolerance Test in Healthy Young Adults

Continuous glucose monitoring devices measure glucose in interstitial fluid. The devices are effective when used by patients with type 1 and 2 diabetes but are increasingly being used by researchers who are interested in the effects of various behaviours of glucose concentrations in healthy participants. Despite their more frequent application in this setting, the devices have not yet been validated for use under such conditions. A total of 124 healthy participants were recruited to a ten-day laboratory study. Each participant underwent four oral glucose tolerance tests, and a total of 3315 out of a possible 4960 paired samples were included in the final analysis. Bland-Altman plots and mean absolute relative differences were used to determine the agreement between the two methods. Bland-Altman analyses revealed that the continuous glucose monitoring devices had proportional bias (R = 0.028, p < 0.001) and a mean bias of -0.048 mmol/L, and device measurements were more variable as glucose concentrations increased. Ninety-nine per cent of paired values were in Zones A and B of the Parkes Error Grid plot, and there was an overall mean absolute relative difference of 16.2% (±15.8%). There was variability in the continuous glucose monitoring devices, and this variability was higher when glucose concentrations were higher. If researchers were to use continuous glucose monitoring devices to measure glucose concentrations during an oral glucose tolerance test in healthy participants, this variability would need to be considered.

Machine learning-based glucose prediction with use of continuous glucose and physical activity monitoring data: The Maastricht Study

BACKGROUND

Closed-loop insulin delivery systems, which integrate continuous glucose monitoring (CGM) and algorithms that continuously guide insulin dosing, have been shown to improve glycaemic control. The ability to predict future glucose values can further optimize such devices. In this study, we used machine learning to train models in predicting future glucose levels based on prior CGM and accelerometry data.

METHODS

We used data from The Maastricht Study, an observational population-based cohort that comprises individuals with normal glucose metabolism, prediabetes, or type 2 diabetes. We included individuals who underwent >48h of CGM (n = 851), most of whom (n = 540) simultaneously wore an accelerometer to assess physical activity. A random subset of individuals was used to train models in predicting glucose levels at 15- and 60-minute intervals based on either CGM data or both CGM and accelerometer data. In the remaining individuals, model performance was evaluated with root-mean-square error (RMSE), Spearman's correlation coefficient (rho) and surveillance error grid. For a proof-of-concept translation, CGM-based prediction models were optimized and validated with the use of data from individuals with type 1 diabetes (OhioT1DM Dataset, n = 6).

RESULTS

Models trained with CGM data were able to accurately predict glucose values at 15 (RMSE: 0.19mmol/L; rho: 0.96) and 60 minutes (RMSE: 0.59mmol/L, rho: 0.72). Model performance was comparable in individuals with type 2 diabetes. Incorporation of accelerometer data only slightly improved prediction. The error grid results indicated that model predictions were clinically safe (15 min: >99%, 60 min >98%). Our prediction models translated well to individuals with type 1 diabetes, which is reflected by high accuracy (RMSEs for 15 and 60 minutes of 0.43 and 1.73 mmol/L, respectively) and clinical safety (15 min: >99%, 60 min: >91%).

CONCLUSIONS

Machine learning-based models are able to accurately and safely predict glucose values at 15- and 60-minute intervals based on CGM data only. Future research should further optimize the models for implementation in closed-loop insulin delivery systems.

A Multicenter Evaluation of a Point-of-Care Blood Glucose Meter System in Critically Ill Patients

BACKGROUND

Our purpose was to evaluate the performance of the ACCU-CHEK® Inform II blood glucose monitoring system (Roche Diagnostics GmbH) compared with the perchloric acid hexokinase (PCA-HK) comparator method on the cobas® 6000 analyzer (Roche Diagnostics International Ltd) in critically ill patients.

METHODS

Overall, 476 arterial (376 pediatric/adult, 100 neonate), 375 venous, and 100 neonatal heel-stick whole-blood samples were collected and evaluated from critical care settings at 10 US hospitals, including the emergency department, medical and surgical intensive care units (ICUs), and neonatal and pediatric ICUs. The ACCU-CHEK Inform II system was evaluated at 2 cutoff boundaries: boundary 1 was ≥95% of results within ±12 mg/dL of the reference (samples with blood glucose <75 mg/dL) or ±12% of the reference (glucose ≥75 mg/dL), and boundary 2 was ≥98% of results within ±15 mg/dL or ±15% of the reference. Clinical performance was assessed by evaluating sample data using Parkes error grid, Monte Carlo simulation, and sensitivity and specificity analyses to estimate clinical accuracy and implications for insulin dosing when using the ACCU-CHEK Inform II system.

RESULTS

Proportions of results within evaluation boundaries 1 and 2, respectively, were 96% and 98% for venous samples, 94% and 97% for pediatric and adult arterial samples, 84% and 98% for neonatal arterial samples, and 96% and 100% for neonatal heel-stick samples. Clinical evaluation demonstrated high specificity and sensitivity, with low risk of potential insulin-dosing errors.

CONCLUSIONS

The ACCU-CHEK Inform II system demonstrated clinically acceptable performance against the PCA-HK reference method for blood glucose monitoring in a diverse population of critically ill patients in US care settings.

Assessment of the accuracy of an intermittent-scanning continuous glucose monitoring device in patients with type 2 diabetes mellitus undergoing hemodialysis (AIDT2H) study

FreeStyle Libre has been approved for use in patients undergoing hemodialysis (HD) in Japan, unlike Europe and the United States; however, evidence regarding its accuracy in such patients is sparse. Forty‐one participants with type 2 diabetes undergoing HD were recruited. The overall mean absolute relative difference and mean absolute difference were 23.4% and 33.9 mg/dL, respectively. Sensor glucose levels and capillary glucose levels were significantly correlated (r = 0.858, P < .01), although the sensor glucose levels were significantly lower than the capillary glucose levels. The accuracy of FreeStyle Libre in patients undergoing HD became deteriorated with the days of usage. The percentage of sensor results in Zones A and B in the consensus error grid analysis and in the Clarke error grid analysis were 99.7% and 99.0%, respectively. Its insufficient accuracy necessitates adjunct usage of FreeStyle Libre with self‐monitoring of blood glucose in patients undergoing HD.

Applying Parkes Grid Method to Evaluate Impact of Variation in Blood Glucose Monitoring (BGM) Strip Accuracy Performance in Type 1 Diabetes Highlights the Potential for Amplification of Imprecision With Less Accurate BGM Strips

BACKGROUND

The National Health Service spends £170 million on blood glucose monitoring (BGM) strips each year and there are pressures to use cheaper less accurate strips. Technology is also being used to increase test frequency with less focus on accuracy.Previous modeling/real-world data analysis highlighted that actual blood glucose variability can be more than twice blood glucose meter reported variability (BGMV). We applied those results to the Parkes error grid to highlight potential clinical impact.

METHOD

BGMV is defined as the percent of deviation from reference that contains 95% of results. Four categories were modeled: laboratory (<5%), high accuracy strips (<10%), ISO 2013 (<15%), and ISO 2003 (<20%) (includes some strips still used).The Parkes error grid model with its associated category of risk including "alter clinical decision" and "affect clinical outcomes" was used, with the profile of frequency of expected results fitted into each BGM accuracy category.

RESULTS

Applying to single readings, almost all strip accuracy ranges derived in a controlled setting fell within the category: clinically accurate/no effect on outcomes areas.However modeling the possible blood glucose distribution in more detail, 30.6% of longer term results of the strips with current ISO accuracy would fall into the "alter clinical action" category. For previous ISO strips, this rose to 44.1%, and for the latest higher accuracy strips, this fell to 12.8%.

CONCLUSION

There is a minimum standard of accuracy needed to ensure that clinical outcomes are not put at risk. This study highlights the potential for amplification of imprecision with less accurate BGM strips.

Comparison of Interstitial Fluid Glucose Levels Obtained by Continuous Glucose Monitoring and Flash Glucose Monitoring in Patients With Type 2 Diabetes Mellitus Undergoing Hemodialysis

BACKGROUND

The accuracy of flash glucose monitoring (FGM, FreeStyle Libre Pro [FSL-Pro]) remains unclear in patients with type 2 diabetes mellitus (T2DM) undergoing hemodialysis.

METHODS

We assessed 13 patients with T2DM undergoing hemodialysis. They simultaneously underwent FGM, continuous glucose monitoring (CGM, iPro2), and self-monitoring blood glucose (SMBG).

RESULTS

Parkes error grid analysis against SMBG showed that 49.0% and 51.0% of interstitial fluid glucose (ISFG) levels measured using FGM and 93.3% and 6.7% of those measured using CGM fell into zones A and B, respectively. Mean absolute relative difference (MARD) against SMBG for FGM was significantly higher than that for CGM (19.5% ± 13.2% vs 8.1% ± 7.6%, P < .0001). Parkes error grid analysis of 2496 paired ISFG levels between FGM and CGM showed that 53.6%, 46.2%, and 0.2% of the plots fell into zones A, B, and C, respectively. Mean ISFG levels were lower with FGM than with CGM (143.7 ± 67.2 mg/dL vs 164.6 ± 58.5 mg/dL; P < .0001). Mean absolute relative difference of ISFG levels between FGM and CGM was 19.2% ± 13.8%. Among three groups classified according to CGM ISFG levels (hypoglycemia, <70 mg/dL; euglycemia, 70-180 mg/dL; and hyperglycemia, >180 mg/dL), the MARDs for hypoglycemia (31.9% ± 25.0%) and euglycemia (22.8% ± 14.6%) were significantly higher than MARD for hyperglycemia (13.0% ± 8.5%) (P < .0001 in both).

CONCLUSIONS

Flash glucose monitoring may be clinically acceptable. Average ISFG levels were lower with FGM than with CGM, and MARDs were higher for hypoglycemia and euglycemia in patients with T2DM undergoing hemodialysis.

The Diabits App for Smartphone-Assisted Predictive Monitoring of Glycemia in Patients With Diabetes: Retrospective Observational Study

BACKGROUND

Diabetes mellitus, which causes dysregulation of blood glucose in humans, is a major public health challenge. Patients with diabetes must monitor their glycemic levels to keep them in a healthy range. This task is made easier by using continuous glucose monitoring (CGM) devices and relaying their output to smartphone apps, thus providing users with real-time information on their glycemic fluctuations and possibly predicting future trends.

OBJECTIVE

This study aims to discuss various challenges of predictive monitoring of glycemia and examines the accuracy and blood glucose control effects of Diabits, a smartphone app that helps patients with diabetes monitor and manage their blood glucose levels in real time.

METHODS

Using data from CGM devices and user input, Diabits applies machine learning techniques to create personalized patient models and predict blood glucose fluctuations up to 60 min in advance. These predictions give patients an opportunity to take pre-emptive action to maintain their blood glucose values within the reference range. In this retrospective observational cohort study, the predictive accuracy of Diabits and the correlation between daily use of the app and blood glucose control metrics were examined based on real app users' data. Moreover, the accuracy of predictions on the 2018 Ohio T1DM (type 1 diabetes mellitus) data set was calculated and compared against other published results.

RESULTS

On the basis of more than 6.8 million data points, 30-min Diabits predictions evaluated using Parkes Error Grid were found to be 86.89% (5,963,930/6,864,130) clinically accurate (zone A) and 99.56% (6,833,625/6,864,130) clinically acceptable (zones A and B), whereas 60-min predictions were 70.56% (4,843,605/6,864,130) clinically accurate and 97.49% (6,692,165/6,864,130) clinically acceptable. By analyzing daily use statistics and CGM data for the 280 most long-standing users of Diabits, it was established that under free-living conditions, many common blood glucose control metrics improved with increased frequency of app use. For instance, the average blood glucose for the days these users did not interact with the app was 154.0 (SD 47.2) mg/dL, with 67.52% of the time spent in the healthy 70 to 180 mg/dL range. For days with 10 or more Diabits sessions, the average blood glucose decreased to 141.6 (SD 42.0) mg/dL (P<.001), whereas the time in euglycemic range increased to 74.28% (P<.001). On the Ohio T1DM data set of 6 patients with type 1 diabetes, 30-min predictions of the base Diabits model had an average root mean square error of 18.68 (SD 2.19) mg/dL, which is an improvement over the published state-of-the-art results for this data set.

CONCLUSIONS

Diabits accurately predicts future glycemic fluctuations, potentially making it easier for patients with diabetes to maintain their blood glucose in the reference range. Furthermore, an improvement in glucose control was observed on days with more frequent Diabits use.

Use of the "FreeStyle Libre" glucose monitoring system in diabetic cats

The aim of this study was to assess the "FreeStyle Libre" flash glucose monitoring system regarding its measurement accuracy and tolerability in cats. Results from 66 sensors applied to 34 predominantly diabetic cats are included. The behaviour during the attachment, wearing, and removal of the sensor and the skin site of attachment were assessed. Blood samples were regularly collected for comparative measurements (hexokinase method). Minimal signs of discomfort were noted, although the sensor was additionally fixed using individual skin stitches. Sensors, which stopped working in situ (70% [46/66]), had a median functional life of 8.3 (1.6-14) days. Skin reactions on the adhesive surface occurred after removal of 39% (23) of 59 sensors with assessable skin reaction (mild erythema: n = 21; superficial dermatitis: n = 2). Due to the upper limit of the measurement range of 27.8 mmol/l (500 mg/dl), the reading device displayed "Hi" in 62% (17/34) of cats repeatedly and/or for periods >1 h. Results were highly correlated with those of the reference method (r_S = 0.90, n = 359). 67.7% (243/359) of the "FreeStyle Libre" measurement values had a maximum deviation of 15% from reference measurements and 99.4% (357/359) were within the zones A and B of Parkes Consensus error grid analysis. In conclusion, the device proved to be practicable, less stressful for the animals and generated in general acceptable results. Although the upper limit of the measurement range is a limiting factor, the device promises to significantly facilitate the management of diabetic cats.

System Accuracy Assessment of a Combined Invasive and Noninvasive Glucometer

BACKGROUND

The pain associated with pricking the fingertip for blood glucose self-testing is considered to be a major burden in diabetes treatment. This study was performed to evaluate the system accuracy of the invasive TensorTip Combo Glucometer (CoG) device component in accordance with ISO15197:2015 requirements and to explore the accuracy of the noninvasive tissue glucose prediction component.

METHODS

One hundred samples were obtained from people with type 1 and type 2 diabetes and healthy volunteers (43 females, 57 males; age: 53 ± 16 years), with glucose distribution as requested by the ISO standard. Three strip lots were tested twice by healthcare professionals in comparison to YSI 2300 Stat Plus reference method followed by a noninvasive tissue glucose reading (NI-CoG). Mean Absolute (Relative) Difference (MARD) was calculated and a consensus error grid (CEG) analysis was performed.

RESULTS

The ISO system accuracy criteria were met with the invasive strip technology by 586/600 of the data points (97.1%) and for each strip lot separately. All invasive results (100%) were within CEG-zone A and total MARD was calculated to be 7.1%. With the noninvasive reading, 99% of raw data points were in A + B (91.1% and 7.8%), and the total MARD was calculated to be 18.1%.

DISCUSSION

The invasive component of the CoG device was shown to be in full compliance with the current ISO15197 criteria. Good results were also obtained with the NI-CoG tissue glucose prediction. This noninvasive technology would potentially be suitable for frequent pain-free glucose monitoring in many people with diabetes.

Evaluation of a near-infrared light ultrasound system as a non-invasive blood glucose monitoring device

The aim of this study was to evaluate the newly developed non-invasive blood glucose system NIRLUS® (Near-Infra Red Light Ultra Sound; NIRLUS Engineering AG, Lübeck, Germany) under standardized conditions. Seventeen healthy men of normal weight (body mass index 22.4 ± 1.4 kg/m² ), aged 18 to 45 years, were enrolled in this study. During an intravenous glucose tolerance test, blood glucose profiles were measured simultaneously using the NIRLUS system and a "gold standard" laboratory reference system. Correlation analysis revealed a strong association between NIRLUS and reference values (r = 0.934; P < 0.001). Subsequent Bland-Altman analysis showed a symmetric distribution (r = 0.047; P = 0.395), and 95.5% of the NIRLUS-reference pairs were within the difference (d) of d ± 2 SD. The median deviation of all paired NIRLUS-reference values was 0.5 mmol/L and the mean percent deviation was 11.5%. Error grid analysis showed that 93.6% of NIRLUS-reference pairs are located in the area A, and 6.4% in the area B. No data were allocated in the areas C to E. This proof-of-concept study demonstrates the reproducibility of accurate blood glucose measures obtained by NIRLUS as compared to a gold standard laboratory reference system. The technology of NIRLUS is an important step forward in the development of non-invasive glucose monitoring.

Flash Glucose Monitoring Can Accurately Reflect Postprandial Glucose Changes in Healthy Adults in Nutrition Studies

OBJECTIVE

This study investigated the accuracy of a flash glucose monitoring system (FGMS) in a postprandial setting.

METHODS

Ten fasted adults without diabetes wore the FGMS sensors then consumed a standard breakfast. Their glucose levels were subsequently recorded for 2 hours, both by the FGMS and by measuring capillary glucose levels using the glucose oxidase method. The accuracy of the FGMS data was assessed using the accuracy limits stated in ISO 15197:2013.

RESULTS

FGMS measurements were mostly lower than glucose oxidase measurements (mean absolute relative difference ± SD: 25.4 ± 17.0%, p < 0.001). However, the maximum difference from baseline captured by the two methods was not significantly different (mean ± SD, glucose oxidase: 58.5 ± 18.9 mg/dl; FGMS, 54.4 ± 28.9 mg/dl, p = 0.366).

CONCLUSIONS

FGMS could track the incremental glycaemic excursions after meals in adults without diabetes, yet further studies with greater sample sizes are needed to confirm this finding.

Patient Satisfaction With a New, High Accuracy Blood Glucose Meter That Provides Personalized Guidance, Insight, and Encouragement

BACKGROUND

Accurate self-monitoring of blood glucose (SMBG) is a key component of effective self-management of glycemic control.

METHODS

The OneTouch Verio Reflect and OneTouch Ultra Plus Reflect BG monitoring systems were evaluated for accuracy in a clinical setting. Subjects also used the meters for a one-week trial period and reported their level of satisfaction with meter features.

RESULTS

Both systems were accurate over a wide glucose range and met lay user and system accuracy BG standards described in ISO15197:2015. Subjects felt that the features of a meter with a dynamic color range indicator and personalized guidance, insight, and encouragement could provide significant benefits to them in the management of their diabetes.

CONCLUSIONS

Both meter systems were accurate over a wide glucose range and the features of the meter and messages were well received by patients in a short take-home trial.

CLINICAL TRIAL REGISTRATION

Clinicaltrials.gov NCT0351542.

The Accuracy and Precision of the Continuously Stored Data from Flash Glucose Monitoring System in Type 2 Diabetes Patients during Standard Meal Tolerance Test

BACKGROUND

The purpose of this study was to investigate the accuracy of the continuously stored data from the Abbott FreeStyle Libre flash glucose monitoring (FGM) system in Chinese diabetes patients during standard meal tests when glucose concentrations were rapidly changing. Subjects and Methods. Interstitial glucose levels were monitored for 14 days in 26 insulin-treated patients with type 2 diabetes using the FGM system. Standard meal tests were conducted to induce large glucose swings. Venous blood glucose (VBG) was tested at 0, 30, 60, and 120 min after standard meal tests in one middle day of the first and second weeks, respectively. The corresponding sensor glucose values were obtained from interpolating continuously stored data points. Assessment of accuracy was according to recent consensus recommendations with median absolute relative difference (MARD) and Clarke and Parkes error grid analysis (CEG and PEG).

RESULTS

Among 208 paired sensor-reference values, 100% were falling within zones A and B of the Clarke and Parkes error grid analysis. The overall MARD was 10.7% (SD, 7.8%). Weighted least squares regression analysis resulted in high agreement between the FGM sensor glucose and VBG readings. The overall MTT results showed that FGM was lower than actual VBG, with MAD of 22.1 mg/dL (1.2 mmol/L). At VBG rates of change of -1 to 0, 0 to 1, 1 to 2, and 2 to 3 mg/dl/min, MARD results were 11.4% (SD, 8.7%), 9.4% (SD, 6.5%), 9.9% (SD, 7.5%), and 9.5% (SD, 7.7%). At rapidly changing VBG concentrations (>3 mg/dl/min), MARD increased to 19.0%, which was significantly higher than slow changing BG groups.

CONCLUSIONS

Continuously stored interstitial glucose measurements with the FGM system were found to be acceptable to evaluate VBG in terms of clinical decision during standard meal tests. The continuously stored data from the FGM system appeared to underestimate venous glucose and performed less well during rapid glucose changes.

System accuracy assessments with a blood glucose meter with combined glucose and ß-hydroxybutyrate measurement capabilities

Background: We evaluated the Wellion Galileo GLU/KET blood and ketone (ß-Hydroxybutyrate, ß-OHB) meter to demonstrate that it meets ISO15107:2015 regulatory approval criteria. Research Design and Methods: A total of 100 subjects (52 female, age: 30 to 84 years, diabetes: 10 type 1/90 type 2) with blood glucose levels distributed over the entire measurement range as required by the ISO15197 protocol were tested (double determinations with 3 strip lots and two devices). A similar test protocol was followed to test ß-OHB strip performance (reference devices: YSI 2300plus for glucose and STANBIO ß-HOB LiquiColor TestKit for ß-OHB). Precision was tested for glucose with 3 blood glucose concentrations (ß-OHB: 2 control solutions). Results: All glucose test-strip lots met the strict ISO acceptance criteria. Mean absolute relative difference (MARD) was 4.9% and all data pairs were in zone A of the consensus error grid. The ß-OHB test-strips also met the pre-defined acceptance criteria. Within-run and between-run precision was calculated to be 2.3% and 0.7% for the glucose strips (3.7%/0.8% for the ketone strips). Conclusions: When tested according to the ISO15197:2015 guideline, the device showed very accurate measurement performance for glucose and ß-OHB testing and fully met regulatory accuracy approval criteria.

An insulin-dose error assessment grid: A new tool to evaluate glucose meter performance

OBJECTIVE

To develop a tool to assess the clinical accuracy of glucose meter performance using an insulin dosing protocol to assess the frequency and extent of error in insulin dose categories.

METHODS

Retrospective comparison of 1815 glucose meter and central laboratory glucose results obtained from 1698 critically ill patients was conducted using the Parkes error grid, Surveillance error grid and an insulin dose error assessment grid with a sliding scale insulin dosing protocol used to manage critically ill patients.

RESULTS

Parkes error grid and Surveillance error grid analyses indicated little risk conferred with the glucose meter results. Insulin dose error assessment grid complemented the aforementioned consensus error grids by determining quantifiable metrics, insulin dose category errors. Insulin dose error analysis indicated that 76.8% (1395/1815) would not have any change in insulin dose, 99.2% (1800/1815) within ±1 insulin dose category, 99.9% (1814/1815) within ±2 categories and 100% within ±3 insulin dose categories.

CONCLUSIONS

Analysis with an insulin dose error grid provides information about the frequency and extent of insulin dose category errors with a specific insulin dosing protocol and describes potential clinical impact of glucose meter error.

The accuracy evaluation of four blood glucose monitoring systems according to ISO 15197:2003 and ISO 15197:2013 criteria

Self-monitoring of blood glucose is recommended to monitor glycaemic levels for the prevention and treatment of diabetes mellitus.The accuracy of blood glucose monitoring systems (BGMS) directly affects the health of the patients. In this study, we evaluated the accuracy of four different blood glucose monitoring systems, including Gold-Accu Meter, Nova StatStrip Xpress^® Glucose Hospital Meter, Freestyle Optium Neo H^® and Accu-Chek Performa according to ISO15197:2003 and ISO15197:2013 criteria. We found that four brands of BGMS (eight BGMS) all met the minimum requirements for system accuracy performance of ISO 15197:2003 and the accuracy criteria of ISO 15197:2013. All results of BGMS lie within zones A and B of the Consensus Error Grid (CEG). In conclusion, all BGMS met the accuracy criteria of ISO 15197:2003 and the accuracy criteria of ISO 15197:2013.

Comparison of glucose monitoring between Freestyle Libre Pro and iPro2 in patients with diabetes mellitus

AIMS/INTRODUCTION

Flash and continuous glucose monitoring systems are becoming prevalent in clinical practice. We directly compared a flash glucose monitoring system (FreeStyle Libre Pro [FSL-Pro]) with a continuous glucose monitoring system (iPro2) in patients with diabetes mellitus.

MATERIALS AND METHODS

Glucose concentrations were simultaneously measured using the FSL-Pro, iPro2 and self-monitoring blood glucose in 10 patients with diabetes mellitus, and agreement among them was assessed.

RESULTS

Parkes error grid analysis showed that the 92.9 and 7.1% of glucose values measured using the FSL-Pro fell into areas A and B, respectively, and that 96.3, 2.8 and 0.9% of those determined using iPro2 fell into areas A, B and C, respectively. The median absolute relative differences compared with self-monitoring blood glucose were 8.1% (3.9-12.7%) and 5.0% (2.6-9.1%) for the FSL-Pro and iPro2, respectively. Analysis of 5,555 paired values showed a close correlation between FSL-Pro and iPro2 glucose values (ρ = 0.96, P < 0.01). Notably, 65.3% of all glucose values were lower for the FSL-Pro than the iPro2. Median glucose values also decreased by 3.3% for the FSL-Pro compared with the iPro2 (177.0 [133.0-228.0] vs 183.0 [145.0-230.0] mg/dL, P < 0.01). The difference in glucose values between the two systems was more pronounced in hypoglycemia. The median absolute relative difference between FSL-Pro and iPro2 during hypoglycemia was much larger than that during euglycemia and hyperglycemia.

CONCLUSIONS

Both the FSL-Pro and iPro2 systems are clinically acceptable, but glucose values tended to be lower when measured using the FSL-Pro than the iPro2. Agreement was not close between these systems during hypoglycemia.